Grasshoppers mainly threaten natural grassland vegetation and crops. Therefore, it is of great significance to understand the relationship between environmental factors and grasshopper occurrence. This paper studies the spatial distribution and key factors of grasshopper occurrence in two grass types by integrating a machine learning model (Maxent) and remote sensing data within the major grasshopper occurrence areas of Inner Mongolia, China. The modelling results demonstrate that the typical steppe has larger suitable area and more proportion for grasshopper living than meadow steppe. The soil type, above biomass, altitude and temperature mainly determine the grasshopper occurrence in typical steppe and meadow steppe. However, the contribution of these factors in the two grass types is significantly different. In addition, related vegetation and meteorological factors affect the different growing stages of grasshoppers between the two grass types. This study clearly defines the different effects of key environmental factors (meteorology, vegetation, soil and topography) for grasshopper occurrence in typical steppe and meadow steppe. It also provides a methodology to guide early warning and precautions for grasshopper pest prevention. The findings of this study will be helpful for future management measures, to ensure grass ecological environment security and the sustainable development of grassland.

O. decorus asiaticus is a major grasshopper species that harms the development of agriculture on the Mongolian Plateau. Therefore, it is important to enhance the monitoring of O. decorus asiaticus. In this study, the spatiotemporal variation in the habitat suitability for O. decorus asiaticus on the Mongolian Plateau was assessed using maximum entropy (Maxent) modeling along with multi-source remote sensing data (meteorology, vegetation, soil, and topography). The predictions of the Maxent model were accurate (AUC = 0.910). The key environmental variables affecting the distribution of grasshoppers and their contribution were grass type (51.3%), accumulated precipitation (24.9%), altitude (13.0%), vegetation coverage (6.6%), and land surface temperature (4.2%). Based on the assessment results of suitability by Maxent model, the model threshold settings, and the formula for calculating the inhabitability index, the 2000s, 2010s, and 2020s inhabitable areas were calculated. The results show that the distribution of suitable habitat for O. decorus asiaticus in 2000 was similar to that in 2010. From 2010 to 2020, the suitability of the habitat for O. decorus asiaticus in the central region of the Mongolian Plateau changed from moderate to high. The main factor contributing to this change was accumulated precipitation. Few changes in the areas of the habitat with low suitability were observed across the study period. The results of this study enhance our understanding of the vulnerability of different regions on the Mongolian Plateau to plagues of O. decorus asiaticus and will aid the monitoring of grasshopper plagues in this region.

Grasshopper populations can quickly grow to catastrophic levels, causing a huge amount of damage in a short time. Oedaleus decorus asiaticus (Bey-Bienko) (O. d. asiaticus) is the most serious species in Xilingol League of the Inner Mongolia Autonomous Region. The region is not only an important grassland but also a site of agricultural heritage systems in China. Therefore, projecting the potential geographic distribution of O. d. asiaticus to provide an early warning is vital. Here, we combined temperature, precipitation, soil, vegetation, and topography with remote sensing data to screen the predictors that best characterize the current geographical distribution of O. d. asiaticus. A MaxEnt model approach was applied to project the potential suitable distribution of O. d. asiaticus in Xilingol League (the Inner Mongolia Autonomous Region of China) combined with a set of optimized parameters. The modeling results indicated that there were six main habitat factors that determined the suitable distribution of O. d. asiaticus such as the soil type (ST), grassland type (GT), elevation, precipitation during the growing period (GP), precipitation during the spawning period (SP), and normalized difference vegetation index during the overwintering period (ONDVI). The simulated result was good, with average AUC and TSS values of 0.875 and 0.812, respectively. The potential inhabitable areas of grasshoppers were 198,527 km², distributed mainly in West Urumqi, Xilinhot City, East Urumqi, Abaga Banner, and Xianghuang Banner of Xilingol League. This study is valuable to guide managers and decision-makers to prevent and control the occurrence of O. d. asiaticus early on and this study may facilitate meaningful reductions in pesticide application.

An increase in grassland rodent pests in China has seriously affected grassland ecological environments and the development of husbandry. Here, we used remote sensing data and a species–environmental matching model to predict the potential spatial distribution of the five major rodent pest species (Microtus, Citellus, Myospalax, Meriones, Ochotona) in northern China, and examined how the predicted suitability of the area depends on environmental variables. The results were consistent and significant, better than random, and close to optimal. Meriones and Microtus had the largest areas of High Suitability and Moderate Suitability with regard to environmental conditions. The combination analysis of areas of Moderate Suitability and High Suitability showed that for 66% of the total area, conditions were suitable for just one rodent species, while conditions suitable for two and three kinds of rodents accounted for 31% and 3%, respectively. Altitude, land surface temperature in winter (November, December, February) and summer (May, June, July), vegetation cover in summer (July, August), and precipitation from spring to summer (April, May, June) determined the spatial distribution of grassland rodents. Our findings provide a powerful and useful methodological tool for tracking the five major rodent pest species in northern China and for future management measures to ensure grassland ecological environment security.

As a perennial plant with long productive span of 30–50 years, grapevine may experience cross-lifespan climate change, which can modify wine quality and challenge viticultural sustainability. Therefore, it is essential to evaluate the viticultural suitability by considering both current and future climate conditions. To this end, a maximum entropy model was proposed to delimitate potentially suitable areas for viticulture based on multi-source data in a novel wine region, Ningxia, China, considering both current and future climate conditions. Firstly, we combined traditional data of climate, soil, and topography with remote sensing data to screen predictors that best characterize current geographical distribution of vineyards. Then, we used those predictors to assess current suitability (2001–2020) in Ningxia. The results indicated altitude, aridity index during April–September (K0409), precipitation during July–September (P0709), normalized difference vegetation index during July–September (NDVI0709), soil organic carbon (SOC), and precipitation in September (P09) were key predictors to assess potential suitability for viticulture, and their threshold values ranged from 1075 m to 1648 m, 2.93 to 4.83, 103.1 mm to 164.1 mm, 0.1 to 0.89, 0.07 g/kg to 11 g/kg and 28.4 mm to 45.0 mm, respectively. Suitability maps revealed a total suitable area of 12029 km², among which the highly and moderately suitable areas accounted for 6.1% and 23.1%, respectively. Finally, the alteration in proportion of potential suitable areas due to changing climate was estimated. The potential suitable areas varied from 8742 km² to 10623 km² over the next 40 years (2022–2060) and decreased to 8826–9184 km² under a short-term sustainability (suitable only during current–2040). To further consider long-term and sustainable development of the wine industry (current–2060), total suitable areas dropped by 26.7–29.2% under different climate scenarios compared with current suitable areas (2001–2020). The conclusions provide indispensable guidance for vineyard zoning considering long-term climate change.

This study employed an integrated approach combining multi-source remote sensing data and the MaxEnt model to systematically assess the ecological niche characteristics of the oriental migratory locust (Locusta migratoria manilensis) in Hainan Island, while projecting the evolution of its suitable habitats under both historical and future climate scenarios (up to 2040). Firstly, we synthesized traditional climate, soil, and topography data with remote sensing data to characterize the suitable areas of the oriental migratory locust based on MaxEnt model (with high accuracy of AUC = 0.935 and TSS = 0.76). Subsequently, six dominant environmental variables—precipitation in April (PRE04), precipitation in September (PRE09), maximum temperature in August (TMAX08), minimum temperature in December (TMIN12), NDVI in February (NDVI02), and NDVI in May (NDVI02)—were identified as key predictors. Their threshold values were determined, with PRE04, PRE09, TMAX08, and TMIN12 ranging from 39 to 44 mm, 196 to 223 mm, 31.1 to 32.2 °C, and 17.7 to 18.0 °C in high-suitability zones, respectively. Finally, these six predictors were used to assess habitat suitability across Hainan Island for both the 2001–2020 and 2021–2040 periods. Under historical climate conditions, highly suitable areas (505 km², 1.41% of total land area) were concentrated in the western and northeastern regions, particularly in Dongfang City (46.27%), Ledong Li Autonomous County (32.91%), and Changjiang Li Autonomous County (18.39%). Future projections indicate significant habitat expansion, with total suitable areas increasing by 13.4–42.0% and highly suitable areas reaching 571–831 km² by 2040. The study highlights the critical Dongfang–Danzhou–Ledong region for targeted locust control, providing scientific support for pest management in tropical island ecosystems under climate change.